Cancer is a group of diseases with uncontrollable growth of abnormal cells. Colorectal cancer (CRC) is one of the most common cancers. Increased intake of animal-source foods, sedentary lifestyle, decreased physical activity, and increased prevalence of excess body weight are independently associated with CRC risk. Additional risk factors include heavy alcohol consumption, cigarette smoking, and consumption of red or processed meat. Ultra-processed food (UPF) is made utilizing multiple components and a number of procedures. Soft drinks and salty or sugary snacks typically contain a lot of added sugar, fats, and processed carbohydrates, which negatively affect the balance of the gut bacteria, nutrients, and bioactive substances that are necessary for the prevention of CRC. The aim of this study is to assess the awareness of the general population in Saudi Arabia toward the relationship between UPF and CRC. A cross-sectional questionnaire-based study was conducted in Saudi Arabia between June and December, 2022. The study involved 802 participants, of which 84% consumed UPF and 71% were aware of the link between UPF and CRC. Only 18.3% were familiar with the particular type of UPF and only 29.4% knew how to prepare them. The prevalence of participants who were aware of the link between UPF and CRC was significantly more among the older age groups, people living in the Eastern Region, and those who knew how to manufacture UPF, while the prevalence of awareness was significantly less among those who regularly consumed UPF. The study showed that a significant portion of subjects regularly ate UPF, and only a few were aware of its link to CRC. This highlights the need for greater awareness of the fundamentals of UPF and its impact on health. Governmental organizations should develop a strategy to raise public awareness of excessive UPF use.

Background: Increased intra-abdominal pressure (IAP), autonomic reactions, and anesthetics all contribute to hemodynamic alterations during laparoscopic cholecystectomy. This study’s objectives are to measure noninvasively the intraoperative individual responses in cardiac and systemic hemodynamics, focusing on cardiac output (CO. L/min), stroke volume (SV, mL/min), systemic vascular resistance (SVR, dyn.s.cm⁻⁵), and noninvasive mean arterial blood pressure (MABP, mmHg) during and after peritoneal insufflation (cmH₂O). The secondary objective was to evaluate the utility of EC as an adjunct to standard monitoring and to assess the individual differences. Methods: The CO and associated parameters were continuously and noninvasively monitored with the electrical cardiometry (EC, ICON, Osypka, Berlin Germany). Results: Seventy-three patients showed that when the IAP increased to 13 [IQR: 13-14] cmH₂O, there was an overall percentage decrease in CO (−11.29%), MABP (−9.31%), and SVR (−23.16%) compared to pre induction with minimal changes in heart rate (HR). Individual variation and extreme reactions among certain patients were noted, with CO falling by −47.14% and MABP by −61.59, respectively, which can have major repercussions. Conclusions: The EC enabled real-time, non-invasive CO monitoring and detected significant cardio-hemodynamic changes that conventional monitors could miss. EC can supplement traditional monitors and give attending anesthesiologists access to more of patients’ vital information.

Objectives: The research aims to evaluate the proficiency of medical interns in identifying critical findings on chest X-rays, such as pneumothorax, pleural effusion, pulmonary edema, pneumonia, and other urgent conditions. It also seeks to determine the interns' capability to differentiate between normal and abnormal findings, as well as their ability to prioritize and communicate these findings to senior medical staff promptly. Materials and Methods: A cross-sectional online questionnaire-based form was used to collect data from medical interns across different medical schools around Saudi Arabia. Pearson correlation was carried out to see the relationship between x-ray interpretation scores and degree of confidence. A Fischer exact test was conducted to find an association between the demographic characteristics and diagnostic accuracy of X-rays. Results: A total of 545 medical interns from different universities participated in this study. The lowest diagnosis accuracy (27%) was when identifying a case of left lower lobe pneumonia. On the other hand, the most identified case (60.9%) was COVID-19 pneumonia. In cases of life-threatening conditions, only 13.1% of them have high confidence in their diagnosis of pneumoperitoneum, and 23.8% have high confidence in their diagnosis of pneumothorax. Normal CXR was answered incorrectly by 47.7% of the participants. The performance among recently started interns for some conditions is better than that of recently finished interns. Moreover, there are no significant differences in performance between those who are interested in radiology and those who are not. More than half of the participants advise doing extracurricular courses, practical case-based training in college, and volunteer work in radiology to improve their CXR interpretation skills. Conclusion: The competence of medical interns in interpreting chest X-rays varied across different medical conditions. Only 27.0% of interns correctly identified left lower lobe pneumonia, while 60.9% of them were able to identify COVID-19 pneumonia. Medical interns' competencies have limitations across different medical conditions; therefore, increasing exposure to radiological cases during internships and adequate training in college are recommended.

Background/Objectives: Thalassemias, a hereditary condition commonly linked to chronic anemia, require regular blood transfusions and repeated blood draws for assessments of hemoglobin (Hb) content, which can be uncomfortable. A promising substitute for laboratory hemoglobin testing is non-invasive spectrophotometric hemoglobin (SpHb) monitoring; however, its applicability particularly among blood transfusion-dependent thalassaemic patients needs to be investigated. This study’s primary goal was to investigate the relationships and agreements between SpHb, g/dL, and an automated hematology analyzer (Hb, g/dL) in this particular patient population. The secondary goal was to track how blood transfusions affect SpHb, g/dL, laboratory Hb, and pleth variability index (PVI, %). Methods: In this study, sixty patients were included. A Masimo Radical-7 pulse CO-oximeter was used to measure the SpHb, and a Sysmex XN-1000 hematological analyzer measured the laboratory Hb. Results: The results revealed a significant correlation between SpHb and laboratory Hb (n = 108, r = 0.587, p < 0.001) but also demonstrated that SpHb consistently overestimated laboratory Hb levels, with a mean bias of −1.18 g/dL (95% CI: −1.4344 to −0.9267). The Bland–Altman analysis showed a good degree of reliability between this bias (SpHb–Hb) and laboratory Hb (g/dL), with an Intra Class Correlation (ICC) of 0.613 but with a wide 95% CI ranging from 0.557 to 0.736 (t = 3.817, p < 0.001). The 95% limits of agreement ranged from −3.7893 to +1.4228 g/dL. Conclusions: This significant bias restricted the application of SpHb as a trustworthy method for assessing hemoglobin levels in patients with blood transfusion-dependent thalassemia. Nonetheless, the capability to monitor SpHb and PVI variations during blood transfusions offered a real-time assessment of the impact of transfusions on patients’ hemoglobin levels and volume status.

Background: Epilepsy affects around 50 million people worldwide and remains a major diagnostic challenge, particularly in resource-limited settings. Electroencephalography (EEG) is essential for diagnosis but relies heavily on expert interpretation, often limited by workforce shortages. Artificial intelligence (AI) offers a promising solution to automate EEG interpretation, enhance diagnostic accuracy, and improving diagnostic efficiency. Methods: This retrospective diagnostic validation study was conducted to evaluate the performance of an AI-based system for automated EEG interpretation. A total of 649 EEG recordings from patients aged 1–91 years were analyzed, with expert neurophysiologist interpretations serving as the reference standard. The AI model, developed using a deep learning architecture, was trained to classify EEGs as normal or abnormal and to further categorize findings into epileptiform-focal, epileptiform-generalized, non-epileptiform-focal, and non-epileptiform-diffuse. Performance metrics included sensitivity, specificity, accuracy, area under the ROC curve (AUC), and Cohen’s kappa coefficient for agreement. Results: The model achieved an overall diagnostic accuracy of 93.8% (95% CI: 90.9–96.0) and an AUC of 0.94, demonstrating strong discriminative ability. Sensitivity for abnormal EEG detection was 99.0%, with specificity of 89.7%, PPV of 98.7%, and NPV of 90.0%. Agreement with expert interpretations was κ = 0.87 (p < 0.001), indicating almost perfect concordance. The model maintained robust performance across clinical contexts, with false positives (5.5%) exceeding false negatives (0.5%), reflecting a safety-oriented error profile suited for screening. No statistically significant impact of artifact presence, sleep state, or EEG type was observed on classification accuracy. Conclusions: The model demonstrated high diagnostic accuracy and near-perfect agreement with expert interpreters, highlighting its potential as a clinical decision-support tool for EEG triage and preliminary screening. Integration into real-world workflows could help alleviate workforce shortages, reduce diagnostic delays, and improve early epilepsy detection—particularly in underserved regions. Further refinement, including enhanced artifact handling and diverse dataset validation, will be essential for clinical deployment.